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ABSTRACT In order to better predict climate change effects on plants and their communities, we need to improve our understanding of how various plant traits and community properties respond to warming, as well as what contexts contribute to variation in these responses. To address this knowledge gap, we compiled data from 126 in situ passive experimental warming studies on 13 different plant trait and community property responses. We then collected metadata from these studies to define 9 different study contexts spanning environmental, experimental, and plant‐level scales. We find that, globally, some traits decrease when warmed (e.g., aboveground N content), while others increase (e.g., plant biomass). We also identify contexts that contribute to variation in plant responses to warming, such as latitude, distance from northern range edge, and plant functional group, but the importance of these contexts varies based on the trait or community property measured. For example, as latitude increases, the effect of warming on reproductive traits becomes stronger, but this latitude‐trait relationship did not hold for all traits. Our study highlights how multiple plant traits and community properties respond to warming across the globe, the importance of carefully designing and interpreting the outcomes of climate change experiments, and the need for coordinated warming experiments across varying environmental contexts in order to mechanistically understand and predict plant community responses to climate warming. 

Vegetation plays a crucial role in coastal dune building. Species‐specific plant characteristics can modulate sediment transport and dune shape, but this factor is absent in most dune building numerical models. Here, we develop a new approach to implement species‐specific vegetation characteristics into a process‐based aeolian sediment transport model. Using a three‐step approach, we incorporated the morphological differences of three dune grass species dominant in the US Pacific Northwest coast (European beachgrassAmmophila arenaria, American beachgrassA. breviligulata, and American dune grassLeymus mollis) into the model AeoLiS. First, we projected the tiller frontal area of each grass species onto a high resolution grid and then re‐scaled the grid to account for the associated vegetation cover for each species. Next, we calibrated the bed shear stress in the numerical model to replicate the actual sand capture efficiency of each species, as measured in a previously published wind tunnel experiment. Simulations were then performed to model sand bedform development within the grass canopies with the same shoot densities for all species and with more realistic average field densities. The species‐specific model shows a significant improvement over the standard model by (a) accurately simulating the sand capture efficiency from the wind tunnel experiment for the grass species and (b) simulating bedform morphology representative of each species' characteristic bedform morphology using realistic field vegetation density. This novel approach to dune modeling will improve spatial and temporal predictions of dune morphologic development and coastal vulnerability under local vegetation conditions and variations in sand delivery. 

Abstract Anthropogenic climate warming affects plant communities by changing community structure and function. Studies on climate warming have primarily focused on individual effects of warming, but the interactive effects of warming with biotic factors could be at least as important in community responses to climate change. In addition, climate change experiments spanning multiple years are necessary to capture interannual variability and detect the influence of these effects within ecological communities. Our study explores the individual and interactive effects of warming and insect herbivory on plant traits and community responses within a 7‐year warming and herbivory manipulation experiment in two early successional plant communities in Michigan, USA. We find stronger support for the individual effects of both warming and herbivory on multiple plant morphological and phenological traits; only the timing of plant green‐up and seed set demonstrated an interactive effect between warming and herbivory. With herbivory, warming advanced green‐up, but with reduced herbivory, there was no significant effect of warming. In contrast, warming increased plant biomass, but the effect of warming on biomass did not depend upon the level of insect herbivores. We found that these treatments had stronger effects in some years than others, highlighting the need for multiyear experiments. This study demonstrates that warming and herbivory can have strong direct effects on plant communities, but that their interactive effects are limited in these early successional systems. Because the strength and direction of these effects can vary by ecological context, it is still advisable to include levels of biotic interactions, multiple traits and years, and community type when studying climate change effects on plants and their communities. 

Abstract Understanding the relationship between intraspecific trait variability (ITV) and its biotic and abiotic drivers is crucial for advancing population and community ecology. Despite its importance, there is a lack of guidance on how to effectively sample ITV and reduce bias in the resulting inferences. In this study, we explored how sample size affects the estimation of population‐level ITV, and how the distribution of sample sizes along an environmental gradient (i.e., sampling design) impacts the probabilities of committing Type I and II errors. We investigated Type I and II error probabilities using four simulated scenarios which varied sampling design and the strength of the ITV‐environment relationships. We also applied simulation scenarios to empirical data on populations of the small mammal,Peromyscus maniculatusacross gradients of latitude and temperature at sites in the National Ecological Observatory Network (NEON) in the continental United States. We found that larger sample sizes reduce error rates in the estimation of population‐level ITV for both in silico andPeromyscus maniculatuspopulations. Furthermore, the influence of sample size on detecting ITV‐environment relationships depends on how sample sizes and population‐level ITV are distributed along environmental gradients. High correlations between sample size and the environment result in greater Type I error, while weak ITV–environmental gradient relationships showed high Type II error probabilities. Therefore, having large sample sizes that are even across populations is the most robust sampling design for studying ITV‐environment relationships. These findings shed light on the complex interplay among sample size, sampling design, ITV, and environmental gradients. 

Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity. This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’. 

Climate warming is altering life cycles of ectotherms by advancing phenology and decreasing generation times. Theoretical models provide powerful tools to investigate these effects of climate warming on consumer–resource population dynamics. Yet, existing theory primarily considers organisms with simplified life histories in constant temperature environments, making it difficult to predict how warming will affect organisms with complex life cycles in seasonal environments. We develop a size-structured consumer–resource model with seasonal temperature dependence, parameterized for a freshwater insect consuming zooplankton. We simulate how climate warming in a seasonal environment could alter a key life-history trait of the consumer, number of generations per year, mediating responses of consumer–resource population sizes and consumer persistence. We find that, with warming, consumer population sizes increase through multiple mechanisms. First, warming decreases generation times by increasing rates of resource ingestion and growth and/or lengthening the growing season. Second, these life-history changes shorten the juvenile stage, increasing the number of emerging adults and population-level reproduction. Unstructured models with similar assumptions found that warming destabilized consumer–resource dynamics. By contrast, our size-structured model predicts stability and consumer persistence. Our study suggests that, in seasonal environments experiencing climate warming, life-history changes that lead to shorter generation times could delay population extinctions. 

Disturbance regimes can strongly influence geographic patterns of biodiversity. The types of disturbances and their frequencies can have varying impacts on different dimensions of biodiversity and taxonomic groups, and their influence can also vary with spatial scale. Yet disturbance layers are lacking at sufficiently high spatial resolution and extent to uncover these relationships with biodiversity. We detected disturbances for the conterminous United States from Landsat time series using the established LandTrendr temporal segmentation with a novel secondary classification that incorporates spatial context. We then included these disturbance layers, aggregated to metrics at different temporal and spatial scales, into model of species richness at National Ecological Observatory Network sites. 

Predictions from species distribution models (SDMs) are commonly used in support of environmental decision-making to explore potential impacts of climate change on biodiversity. However, because future climates are likely to differ from current climates, there has been ongoing interest in understanding the ability of SDMs to predict species responses under novel conditions (i.e., model transferability). Here, we explore the spatial and environmental limits to extrapolation in SDMs using forest inventory data from 11 model algorithms for 108 tree species across the western United States. Algorithms performed well in predicting occurrence for plots that occurred in the same geographic region in which they were fitted. However, a substantial portion of models performed worse than random when predicting for geographic regions in which algorithms were not fitted. Our results suggest that for transfers in geographic space, no specific algorithm was better than another as there were no significant differences in predictive performance across algorithms. There were significant differences in predictive performance for algorithms transferred in environmental space with GAM performing best. However, the predictive performance of GAM declined steeply with increasing extrapolation in environmental space relative to other algorithms. The results of this study suggest that SDMs may be limited in their ability to predict species ranges beyond the environmental data used for model fitting. When predicting climate-driven range shifts, extrapolation may also not reflect important biotic and abiotic drivers of species ranges, and thus further misrepresent the realized shift in range. Future studies investigating transferability of process based SDMs or relationships between geodiversity and biodiversity may hold promise.